{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Means Clustering\n",
    "\n",
    "The $K$-means algorithm divides a set of $N$ samples $X$ into $K$ disjoint clusters $C$, each described by the mean $\\mu_j$ of the samples in the cluster. The means are commonly called the **cluster “centroids”**; note that they are not, in general, points from $X$, although they live in the same space. The K-means algorithm aims to choose centroids that minimise the inertia, or within-cluster sum of squared criterion:\n",
    "\n",
    "$$\\sum_{i=0}^{n}\\min_{\\mu_j \\in C}(||x_j - \\mu_i||^2)$$\n",
    "\n",
    "## How the algorithm works\n",
    "\n",
    "The Κ-means clustering algorithm uses iterative refinement to produce a final result. The algorithm inputs are the number of clusters $Κ$ and the data set. The data set is a collection of features for each data point. The algorithms starts with initial estimates for the $Κ$ centroids, which can either be randomly generated or randomly selected from the data set. The algorithm then iterates between two steps:\n",
    "\n",
    "**Data assigment step**: Each centroid defines one of the clusters. In this step, each data point is assigned to its nearest centroid, based on the squared Euclidean distance. More formally, if $c_i$ is the collection of centroids in set $C$, then each data point $x$ is assigned to a cluster based on\n",
    "\n",
    "$$\\underset{c_i \\in C}{\\arg\\min} \\; dist(c_i,x)^2$$\n",
    "where dist( · ) is the standard ($L_2$) Euclidean distance. Let the set of data point assignments for each ith cluster centroid be $S_i$.\n",
    "\n",
    "**Centroid update step**: In this step, the centroids are recomputed. This is done by taking the mean of all data points assigned to that centroid's cluster.\n",
    "\n",
    "$$c_i=\\frac{1}{|S_i|}\\sum_{x_i \\in S_i x_i}$$\n",
    "\n",
    "The algorithm iterates between steps one and two until a stopping criteria is met (i.e., no data points change clusters, the sum of the distances is minimized, or some maximum number of iterations is reached).\n",
    "\n",
    "** Convergence and random initialization **\n",
    "\n",
    "This algorithm is guaranteed to converge to a result. The result may be a local optimum (i.e. not necessarily the best possible outcome), meaning that assessing more than one run of the algorithm with randomized starting centroids may give a better outcome.\n",
    "\n",
    "<img src=https://upload.wikimedia.org/wikipedia/commons/e/ea/K-means_convergence.gif style=\"width: 500px;\"/>\n",
    "\n",
    "## The Data\n",
    "\n",
    "For this project we will attempt to use KMeans Clustering to cluster Universities into to two groups, Private and Public. We will use a data frame with 777 observations on the following 18 variables.\n",
    "\n",
    "* Private A factor with levels No and Yes indicating private or public university\n",
    "* Apps Number of applications received\n",
    "* Accept Number of applications accepted\n",
    "* Enroll Number of new students enrolled\n",
    "* Top10perc Pct. new students from top 10% of H.S. class\n",
    "* Top25perc Pct. new students from top 25% of H.S. class\n",
    "* F.Undergrad Number of fulltime undergraduates\n",
    "* P.Undergrad Number of parttime undergraduates\n",
    "* Outstate Out-of-state tuition\n",
    "* Room.Board Room and board costs\n",
    "* Books Estimated book costs\n",
    "* Personal Estimated personal spending\n",
    "* PhD Pct. of faculty with Ph.D.’s\n",
    "* Terminal Pct. of faculty with terminal degree\n",
    "* S.F.Ratio Student/faculty ratio\n",
    "* perc.alumni Pct. alumni who donate\n",
    "* Expend Instructional expenditure per student\n",
    "* Grad.Rate Graduation rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Read in the College_Data file using read_csv. Figure out how to set the first column as the index.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('College_Data',index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check the head of the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abilene Christian University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1660</td>\n",
       "      <td>1232</td>\n",
       "      <td>721</td>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>2885</td>\n",
       "      <td>537</td>\n",
       "      <td>7440</td>\n",
       "      <td>3300</td>\n",
       "      <td>450</td>\n",
       "      <td>2200</td>\n",
       "      <td>70</td>\n",
       "      <td>78</td>\n",
       "      <td>18.1</td>\n",
       "      <td>12</td>\n",
       "      <td>7041</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adelphi University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>2186</td>\n",
       "      <td>1924</td>\n",
       "      <td>512</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>2683</td>\n",
       "      <td>1227</td>\n",
       "      <td>12280</td>\n",
       "      <td>6450</td>\n",
       "      <td>750</td>\n",
       "      <td>1500</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>12.2</td>\n",
       "      <td>16</td>\n",
       "      <td>10527</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adrian College</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1428</td>\n",
       "      <td>1097</td>\n",
       "      <td>336</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>1036</td>\n",
       "      <td>99</td>\n",
       "      <td>11250</td>\n",
       "      <td>3750</td>\n",
       "      <td>400</td>\n",
       "      <td>1165</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>12.9</td>\n",
       "      <td>30</td>\n",
       "      <td>8735</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agnes Scott College</th>\n",
       "      <td>Yes</td>\n",
       "      <td>417</td>\n",
       "      <td>349</td>\n",
       "      <td>137</td>\n",
       "      <td>60</td>\n",
       "      <td>89</td>\n",
       "      <td>510</td>\n",
       "      <td>63</td>\n",
       "      <td>12960</td>\n",
       "      <td>5450</td>\n",
       "      <td>450</td>\n",
       "      <td>875</td>\n",
       "      <td>92</td>\n",
       "      <td>97</td>\n",
       "      <td>7.7</td>\n",
       "      <td>37</td>\n",
       "      <td>19016</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska Pacific University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>249</td>\n",
       "      <td>869</td>\n",
       "      <td>7560</td>\n",
       "      <td>4120</td>\n",
       "      <td>800</td>\n",
       "      <td>1500</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2</td>\n",
       "      <td>10922</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Private  Apps  Accept  Enroll  Top10perc  \\\n",
       "Abilene Christian University     Yes  1660    1232     721         23   \n",
       "Adelphi University               Yes  2186    1924     512         16   \n",
       "Adrian College                   Yes  1428    1097     336         22   \n",
       "Agnes Scott College              Yes   417     349     137         60   \n",
       "Alaska Pacific University        Yes   193     146      55         16   \n",
       "\n",
       "                              Top25perc  F.Undergrad  P.Undergrad  Outstate  \\\n",
       "Abilene Christian University         52         2885          537      7440   \n",
       "Adelphi University                   29         2683         1227     12280   \n",
       "Adrian College                       50         1036           99     11250   \n",
       "Agnes Scott College                  89          510           63     12960   \n",
       "Alaska Pacific University            44          249          869      7560   \n",
       "\n",
       "                              Room.Board  Books  Personal  PhD  Terminal  \\\n",
       "Abilene Christian University        3300    450      2200   70        78   \n",
       "Adelphi University                  6450    750      1500   29        30   \n",
       "Adrian College                      3750    400      1165   53        66   \n",
       "Agnes Scott College                 5450    450       875   92        97   \n",
       "Alaska Pacific University           4120    800      1500   76        72   \n",
       "\n",
       "                              S.F.Ratio  perc.alumni  Expend  Grad.Rate  \n",
       "Abilene Christian University       18.1           12    7041         60  \n",
       "Adelphi University                 12.2           16   10527         56  \n",
       "Adrian College                     12.9           30    8735         54  \n",
       "Agnes Scott College                 7.7           37   19016         59  \n",
       "Alaska Pacific University          11.9            2   10922         15  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the info() and describe() methods on the data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 777 entries, Abilene Christian University to York College of Pennsylvania\n",
      "Data columns (total 18 columns):\n",
      "Private        777 non-null object\n",
      "Apps           777 non-null int64\n",
      "Accept         777 non-null int64\n",
      "Enroll         777 non-null int64\n",
      "Top10perc      777 non-null int64\n",
      "Top25perc      777 non-null int64\n",
      "F.Undergrad    777 non-null int64\n",
      "P.Undergrad    777 non-null int64\n",
      "Outstate       777 non-null int64\n",
      "Room.Board     777 non-null int64\n",
      "Books          777 non-null int64\n",
      "Personal       777 non-null int64\n",
      "PhD            777 non-null int64\n",
      "Terminal       777 non-null int64\n",
      "S.F.Ratio      777 non-null float64\n",
      "perc.alumni    777 non-null int64\n",
      "Expend         777 non-null int64\n",
      "Grad.Rate      777 non-null int64\n",
      "dtypes: float64(1), int64(16), object(1)\n",
      "memory usage: 115.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3001.638353</td>\n",
       "      <td>2018.804376</td>\n",
       "      <td>779.972973</td>\n",
       "      <td>27.558559</td>\n",
       "      <td>55.796654</td>\n",
       "      <td>3699.907336</td>\n",
       "      <td>855.298584</td>\n",
       "      <td>10440.669241</td>\n",
       "      <td>4357.526384</td>\n",
       "      <td>549.380952</td>\n",
       "      <td>1340.642214</td>\n",
       "      <td>72.660232</td>\n",
       "      <td>79.702703</td>\n",
       "      <td>14.089704</td>\n",
       "      <td>22.743887</td>\n",
       "      <td>9660.171171</td>\n",
       "      <td>65.46332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3870.201484</td>\n",
       "      <td>2451.113971</td>\n",
       "      <td>929.176190</td>\n",
       "      <td>17.640364</td>\n",
       "      <td>19.804778</td>\n",
       "      <td>4850.420531</td>\n",
       "      <td>1522.431887</td>\n",
       "      <td>4023.016484</td>\n",
       "      <td>1096.696416</td>\n",
       "      <td>165.105360</td>\n",
       "      <td>677.071454</td>\n",
       "      <td>16.328155</td>\n",
       "      <td>14.722359</td>\n",
       "      <td>3.958349</td>\n",
       "      <td>12.391801</td>\n",
       "      <td>5221.768440</td>\n",
       "      <td>17.17771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2340.000000</td>\n",
       "      <td>1780.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3186.000000</td>\n",
       "      <td>10.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>776.000000</td>\n",
       "      <td>604.000000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>992.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>7320.000000</td>\n",
       "      <td>3597.000000</td>\n",
       "      <td>470.000000</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>6751.000000</td>\n",
       "      <td>53.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1558.000000</td>\n",
       "      <td>1110.000000</td>\n",
       "      <td>434.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>1707.000000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>9990.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>8377.000000</td>\n",
       "      <td>65.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3624.000000</td>\n",
       "      <td>2424.000000</td>\n",
       "      <td>902.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>4005.000000</td>\n",
       "      <td>967.000000</td>\n",
       "      <td>12925.000000</td>\n",
       "      <td>5050.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>1700.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>10830.000000</td>\n",
       "      <td>78.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>48094.000000</td>\n",
       "      <td>26330.000000</td>\n",
       "      <td>6392.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>31643.000000</td>\n",
       "      <td>21836.000000</td>\n",
       "      <td>21700.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>2340.000000</td>\n",
       "      <td>6800.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>39.800000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>56233.000000</td>\n",
       "      <td>118.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Apps        Accept       Enroll   Top10perc   Top25perc  \\\n",
       "count    777.000000    777.000000   777.000000  777.000000  777.000000   \n",
       "mean    3001.638353   2018.804376   779.972973   27.558559   55.796654   \n",
       "std     3870.201484   2451.113971   929.176190   17.640364   19.804778   \n",
       "min       81.000000     72.000000    35.000000    1.000000    9.000000   \n",
       "25%      776.000000    604.000000   242.000000   15.000000   41.000000   \n",
       "50%     1558.000000   1110.000000   434.000000   23.000000   54.000000   \n",
       "75%     3624.000000   2424.000000   902.000000   35.000000   69.000000   \n",
       "max    48094.000000  26330.000000  6392.000000   96.000000  100.000000   \n",
       "\n",
       "        F.Undergrad   P.Undergrad      Outstate   Room.Board        Books  \\\n",
       "count    777.000000    777.000000    777.000000   777.000000   777.000000   \n",
       "mean    3699.907336    855.298584  10440.669241  4357.526384   549.380952   \n",
       "std     4850.420531   1522.431887   4023.016484  1096.696416   165.105360   \n",
       "min      139.000000      1.000000   2340.000000  1780.000000    96.000000   \n",
       "25%      992.000000     95.000000   7320.000000  3597.000000   470.000000   \n",
       "50%     1707.000000    353.000000   9990.000000  4200.000000   500.000000   \n",
       "75%     4005.000000    967.000000  12925.000000  5050.000000   600.000000   \n",
       "max    31643.000000  21836.000000  21700.000000  8124.000000  2340.000000   \n",
       "\n",
       "          Personal         PhD    Terminal   S.F.Ratio  perc.alumni  \\\n",
       "count   777.000000  777.000000  777.000000  777.000000   777.000000   \n",
       "mean   1340.642214   72.660232   79.702703   14.089704    22.743887   \n",
       "std     677.071454   16.328155   14.722359    3.958349    12.391801   \n",
       "min     250.000000    8.000000   24.000000    2.500000     0.000000   \n",
       "25%     850.000000   62.000000   71.000000   11.500000    13.000000   \n",
       "50%    1200.000000   75.000000   82.000000   13.600000    21.000000   \n",
       "75%    1700.000000   85.000000   92.000000   16.500000    31.000000   \n",
       "max    6800.000000  103.000000  100.000000   39.800000    64.000000   \n",
       "\n",
       "             Expend  Grad.Rate  \n",
       "count    777.000000  777.00000  \n",
       "mean    9660.171171   65.46332  \n",
       "std     5221.768440   17.17771  \n",
       "min     3186.000000   10.00000  \n",
       "25%     6751.000000   53.00000  \n",
       "50%     8377.000000   65.00000  \n",
       "75%    10830.000000   78.00000  \n",
       "max    56233.000000  118.00000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Analysis\n",
    "\n",
    "** Create a scatterplot of Grad.Rate versus Room.Board (and their linear fit) where the points are colored by the Private column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1df639b8710>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VAkfNQDC3agBINFcFv3nTk4QZ86RlwAs0cWMNX2tV8eRD/7Kj8PvjPg/jmIi9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OAAAg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\nf0lw8BlmBMEOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df639b88d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('Room.Board','Grad.Rate',data=df, hue='Private',\n",
    "           palette='coolwarm',size=6,aspect=1,fit_reg=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a scatterplot of F.Undergrad versus Outstate where the points are colored by the Private column.**\n",
    "\n",
    "**The plot shows that these two feature dimensions separate out baed on the type of college**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1df639b8400>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H2rnHjx9X1Ra3OzvBply0vHa5VKKN9tAopvk/SPxsjoZgEDLCBjPklG3zQm/s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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df639b8518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('Outstate','F.Undergrad',data=df, hue='Private',\n",
    "           palette='coolwarm',size=6,aspect=1,fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create a boxplot of student-faculty ratio based on college type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df63e97a20>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df63e2e400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Private',y='S.F.Ratio',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create a boxplot of percent of alumni who donate based on college type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df63e5d6d8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df63f4c4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Private',y='perc.alumni',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create a stacked histogram showing Out of State Tuition based on the Private column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lkjFjxmTNmjVJkhUrVmTs2LG1nRIAAKAX6PIjgr/7u7+ba665Jueff362bduWa6+9Nh/8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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df63fec208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"Private\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'Outstate',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a similar histogram for the Grad.Rate column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CCwAAoBCBBQAAUIjAAgAAKERgAQAAFCKwAAAAChFYAAAAhQgsAACAQgQWAABAIQILAACg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\nYAEAABTy/wDDJx181NqrZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df629affd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"Private\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'Grad.Rate',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There seems to be a private school with a graduation rate of higher than 100%**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cazenovia College</th>\n",
       "      <td>Yes</td>\n",
       "      <td>3847</td>\n",
       "      <td>3433</td>\n",
       "      <td>527</td>\n",
       "      <td>9</td>\n",
       "      <td>35</td>\n",
       "      <td>1010</td>\n",
       "      <td>12</td>\n",
       "      <td>9384</td>\n",
       "      <td>4840</td>\n",
       "      <td>600</td>\n",
       "      <td>500</td>\n",
       "      <td>22</td>\n",
       "      <td>47</td>\n",
       "      <td>14.3</td>\n",
       "      <td>20</td>\n",
       "      <td>7697</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Private  Apps  Accept  Enroll  Top10perc  Top25perc  \\\n",
       "Cazenovia College     Yes  3847    3433     527          9         35   \n",
       "\n",
       "                   F.Undergrad  P.Undergrad  Outstate  Room.Board  Books  \\\n",
       "Cazenovia College         1010           12      9384        4840    600   \n",
       "\n",
       "                   Personal  PhD  Terminal  S.F.Ratio  perc.alumni  Expend  \\\n",
       "Cazenovia College       500   22        47       14.3           20    7697   \n",
       "\n",
       "                   Grad.Rate  \n",
       "Cazenovia College        118  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Grad.Rate'] > 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Set that school's graduation rate to 100 so it makes sense. You may get a warning not an error) when doing this operation, so use dataframe operations or just re-do the histogram visualization to make sure it actually went through.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Tirtha\\Python\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "df['Grad.Rate']['Cazenovia College'] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Private, Apps, Accept, Enroll, Top10perc, Top25perc, F.Undergrad, P.Undergrad, Outstate, Room.Board, Books, Personal, PhD, Terminal, S.F.Ratio, perc.alumni, Expend, Grad.Rate]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Grad.Rate'] > 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAEYrf7wKAACgEIEFAABQiMACAAAoRGABAAAUIrAAAAAKEVgAAACFCCwAAIBCBBYAAEAh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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df6404cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"Private\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'Grad.Rate',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K Means Cluster Creation\n",
    "\n",
    "** Import KMeans from SciKit Learn.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create an instance of a K Means model with 2 clusters.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=2,verbose=0,tol=1e-3,max_iter=300,n_init=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fit the model to all the data except for the Private label.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=2, n_init=20, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.001, verbose=0)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(df.drop('Private',axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What are the cluster center vectors?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.03631389e+04,   6.55089815e+03,   2.56972222e+03,\n",
       "          4.14907407e+01,   7.02037037e+01,   1.30619352e+04,\n",
       "          2.46486111e+03,   1.07191759e+04,   4.64347222e+03,\n",
       "          5.95212963e+02,   1.71420370e+03,   8.63981481e+01,\n",
       "          9.13333333e+01,   1.40277778e+01,   2.00740741e+01,\n",
       "          1.41705000e+04,   6.75925926e+01,   3.14814815e-01],\n",
       "       [  1.81323468e+03,   1.28716592e+03,   4.91044843e+02,\n",
       "          2.53094170e+01,   5.34708520e+01,   2.18854858e+03,\n",
       "          5.95458894e+02,   1.03957085e+04,   4.31136472e+03,\n",
       "          5.41982063e+02,   1.28033632e+03,   7.04424514e+01,\n",
       "          7.78251121e+01,   1.40997010e+01,   2.31748879e+01,\n",
       "          8.93204634e+03,   6.50926756e+01,   7.93721973e-01]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus_cent=kmeans.cluster_centers_\n",
    "clus_cent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now compare these cluster centers (for all dimensions/features) to the known means of labeled data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>565.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>565.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1977.929204</td>\n",
       "      <td>1305.702655</td>\n",
       "      <td>456.945133</td>\n",
       "      <td>29.330973</td>\n",
       "      <td>56.957522</td>\n",
       "      <td>1872.168142</td>\n",
       "      <td>433.966372</td>\n",
       "      <td>11801.693805</td>\n",
       "      <td>4586.143363</td>\n",
       "      <td>547.506195</td>\n",
       "      <td>1214.440708</td>\n",
       "      <td>71.093805</td>\n",
       "      <td>78.534513</td>\n",
       "      <td>12.945487</td>\n",
       "      <td>25.890265</td>\n",
       "      <td>10486.353982</td>\n",
       "      <td>68.966372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2443.341319</td>\n",
       "      <td>1369.549478</td>\n",
       "      <td>457.529136</td>\n",
       "      <td>17.851391</td>\n",
       "      <td>19.588360</td>\n",
       "      <td>2110.661773</td>\n",
       "      <td>722.370487</td>\n",
       "      <td>3707.470822</td>\n",
       "      <td>1089.697557</td>\n",
       "      <td>174.932303</td>\n",
       "      <td>632.879647</td>\n",
       "      <td>17.350886</td>\n",
       "      <td>15.450251</td>\n",
       "      <td>3.518573</td>\n",
       "      <td>12.400755</td>\n",
       "      <td>5682.576587</td>\n",
       "      <td>16.673032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2340.000000</td>\n",
       "      <td>2370.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3186.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>619.000000</td>\n",
       "      <td>501.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>840.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>9100.000000</td>\n",
       "      <td>3736.000000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7477.000000</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1133.000000</td>\n",
       "      <td>859.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>1274.000000</td>\n",
       "      <td>207.000000</td>\n",
       "      <td>11200.000000</td>\n",
       "      <td>4400.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>12.700000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>8954.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2186.000000</td>\n",
       "      <td>1580.000000</td>\n",
       "      <td>520.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>13970.000000</td>\n",
       "      <td>5400.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>11625.000000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20192.000000</td>\n",
       "      <td>13007.000000</td>\n",
       "      <td>4615.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27378.000000</td>\n",
       "      <td>10221.000000</td>\n",
       "      <td>21700.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>2340.000000</td>\n",
       "      <td>6800.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>39.800000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>56233.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Apps        Accept       Enroll   Top10perc   Top25perc  \\\n",
       "count    565.000000    565.000000   565.000000  565.000000  565.000000   \n",
       "mean    1977.929204   1305.702655   456.945133   29.330973   56.957522   \n",
       "std     2443.341319   1369.549478   457.529136   17.851391   19.588360   \n",
       "min       81.000000     72.000000    35.000000    1.000000    9.000000   \n",
       "25%      619.000000    501.000000   206.000000   17.000000   42.000000   \n",
       "50%     1133.000000    859.000000   328.000000   25.000000   55.000000   \n",
       "75%     2186.000000   1580.000000   520.000000   36.000000   70.000000   \n",
       "max    20192.000000  13007.000000  4615.000000   96.000000  100.000000   \n",
       "\n",
       "        F.Undergrad   P.Undergrad      Outstate   Room.Board        Books  \\\n",
       "count    565.000000    565.000000    565.000000   565.000000   565.000000   \n",
       "mean    1872.168142    433.966372  11801.693805  4586.143363   547.506195   \n",
       "std     2110.661773    722.370487   3707.470822  1089.697557   174.932303   \n",
       "min      139.000000      1.000000   2340.000000  2370.000000   250.000000   \n",
       "25%      840.000000     63.000000   9100.000000  3736.000000   450.000000   \n",
       "50%     1274.000000    207.000000  11200.000000  4400.000000   500.000000   \n",
       "75%     2018.000000    541.000000  13970.000000  5400.000000   600.000000   \n",
       "max    27378.000000  10221.000000  21700.000000  8124.000000  2340.000000   \n",
       "\n",
       "          Personal         PhD    Terminal   S.F.Ratio  perc.alumni  \\\n",
       "count   565.000000  565.000000  565.000000  565.000000   565.000000   \n",
       "mean   1214.440708   71.093805   78.534513   12.945487    25.890265   \n",
       "std     632.879647   17.350886   15.450251    3.518573    12.400755   \n",
       "min     250.000000    8.000000   24.000000    2.500000     2.000000   \n",
       "25%     800.000000   60.000000   68.000000   11.100000    16.000000   \n",
       "50%    1100.000000   73.000000   81.000000   12.700000    25.000000   \n",
       "75%    1500.000000   85.000000   92.000000   14.500000    34.000000   \n",
       "max    6800.000000  100.000000  100.000000   39.800000    64.000000   \n",
       "\n",
       "             Expend   Grad.Rate  \n",
       "count    565.000000  565.000000  \n",
       "mean   10486.353982   68.966372  \n",
       "std     5682.576587   16.673032  \n",
       "min     3186.000000   15.000000  \n",
       "25%     7477.000000   58.000000  \n",
       "50%     8954.000000   69.000000  \n",
       "75%    11625.000000   81.000000  \n",
       "max    56233.000000  100.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Private']=='Yes'].describe() # Statistics for private colleges only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5729.919811</td>\n",
       "      <td>3919.287736</td>\n",
       "      <td>1640.872642</td>\n",
       "      <td>22.834906</td>\n",
       "      <td>52.702830</td>\n",
       "      <td>8571.004717</td>\n",
       "      <td>1978.188679</td>\n",
       "      <td>6813.410377</td>\n",
       "      <td>3748.240566</td>\n",
       "      <td>554.377358</td>\n",
       "      <td>1676.981132</td>\n",
       "      <td>76.834906</td>\n",
       "      <td>82.816038</td>\n",
       "      <td>17.139151</td>\n",
       "      <td>14.358491</td>\n",
       "      <td>7458.316038</td>\n",
       "      <td>56.042453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5370.675335</td>\n",
       "      <td>3477.266276</td>\n",
       "      <td>1261.592009</td>\n",
       "      <td>16.180443</td>\n",
       "      <td>20.091058</td>\n",
       "      <td>6467.696087</td>\n",
       "      <td>2321.034696</td>\n",
       "      <td>2145.248389</td>\n",
       "      <td>858.139928</td>\n",
       "      <td>135.729935</td>\n",
       "      <td>677.515680</td>\n",
       "      <td>12.317525</td>\n",
       "      <td>12.069669</td>\n",
       "      <td>3.418049</td>\n",
       "      <td>7.518935</td>\n",
       "      <td>2695.541611</td>\n",
       "      <td>14.583412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>233.000000</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>633.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2580.000000</td>\n",
       "      <td>1780.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3605.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2190.750000</td>\n",
       "      <td>1563.250000</td>\n",
       "      <td>701.750000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3601.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>5366.000000</td>\n",
       "      <td>3121.500000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5715.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4307.000000</td>\n",
       "      <td>2929.500000</td>\n",
       "      <td>1337.500000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>6785.500000</td>\n",
       "      <td>1375.000000</td>\n",
       "      <td>6609.000000</td>\n",
       "      <td>3708.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>78.500000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>17.250000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>6716.500000</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7722.500000</td>\n",
       "      <td>5264.000000</td>\n",
       "      <td>2243.750000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>12507.000000</td>\n",
       "      <td>2495.250000</td>\n",
       "      <td>7844.000000</td>\n",
       "      <td>4362.000000</td>\n",
       "      <td>612.000000</td>\n",
       "      <td>2051.250000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>19.325000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>8570.250000</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>48094.000000</td>\n",
       "      <td>26330.000000</td>\n",
       "      <td>6392.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>31643.000000</td>\n",
       "      <td>21836.000000</td>\n",
       "      <td>15732.000000</td>\n",
       "      <td>6540.000000</td>\n",
       "      <td>1125.000000</td>\n",
       "      <td>4288.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>28.800000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>16527.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Apps        Accept       Enroll   Top10perc   Top25perc  \\\n",
       "count    212.000000    212.000000   212.000000  212.000000  212.000000   \n",
       "mean    5729.919811   3919.287736  1640.872642   22.834906   52.702830   \n",
       "std     5370.675335   3477.266276  1261.592009   16.180443   20.091058   \n",
       "min      233.000000    233.000000   153.000000    1.000000   12.000000   \n",
       "25%     2190.750000   1563.250000   701.750000   12.000000   37.000000   \n",
       "50%     4307.000000   2929.500000  1337.500000   19.000000   51.000000   \n",
       "75%     7722.500000   5264.000000  2243.750000   27.500000   65.000000   \n",
       "max    48094.000000  26330.000000  6392.000000   95.000000  100.000000   \n",
       "\n",
       "        F.Undergrad   P.Undergrad      Outstate   Room.Board        Books  \\\n",
       "count    212.000000    212.000000    212.000000   212.000000   212.000000   \n",
       "mean    8571.004717   1978.188679   6813.410377  3748.240566   554.377358   \n",
       "std     6467.696087   2321.034696   2145.248389   858.139928   135.729935   \n",
       "min      633.000000      9.000000   2580.000000  1780.000000    96.000000   \n",
       "25%     3601.000000    600.000000   5366.000000  3121.500000   500.000000   \n",
       "50%     6785.500000   1375.000000   6609.000000  3708.000000   550.000000   \n",
       "75%    12507.000000   2495.250000   7844.000000  4362.000000   612.000000   \n",
       "max    31643.000000  21836.000000  15732.000000  6540.000000  1125.000000   \n",
       "\n",
       "          Personal         PhD    Terminal   S.F.Ratio  perc.alumni  \\\n",
       "count   212.000000  212.000000  212.000000  212.000000   212.000000   \n",
       "mean   1676.981132   76.834906   82.816038   17.139151    14.358491   \n",
       "std     677.515680   12.317525   12.069669    3.418049     7.518935   \n",
       "min     400.000000   33.000000   33.000000    6.700000     0.000000   \n",
       "25%    1200.000000   71.000000   76.000000   15.100000     9.000000   \n",
       "50%    1649.000000   78.500000   86.000000   17.250000    13.500000   \n",
       "75%    2051.250000   86.000000   92.000000   19.325000    19.000000   \n",
       "max    4288.000000  103.000000  100.000000   28.800000    48.000000   \n",
       "\n",
       "             Expend   Grad.Rate  \n",
       "count    212.000000  212.000000  \n",
       "mean    7458.316038   56.042453  \n",
       "std     2695.541611   14.583412  \n",
       "min     3605.000000   10.000000  \n",
       "25%     5715.000000   46.000000  \n",
       "50%     6716.500000   55.000000  \n",
       "75%     8570.250000   65.000000  \n",
       "max    16527.000000  100.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Private']=='No'].describe() # Statistics for public colleges only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a data frame with cluster centers and with column names borrowed from the original data frame**\n",
    "\n",
    "**Is it clear from this data frame which label corresponds to private college (0 or 1)?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
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       "      <th>P.Undergrad</th>\n",
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       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "      <th>Cluster</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10363.138889</td>\n",
       "      <td>6550.898148</td>\n",
       "      <td>2569.722222</td>\n",
       "      <td>41.490741</td>\n",
       "      <td>70.203704</td>\n",
       "      <td>13061.935185</td>\n",
       "      <td>2464.861111</td>\n",
       "      <td>10719.175926</td>\n",
       "      <td>4643.472222</td>\n",
       "      <td>595.212963</td>\n",
       "      <td>1714.203704</td>\n",
       "      <td>86.398148</td>\n",
       "      <td>91.333333</td>\n",
       "      <td>14.027778</td>\n",
       "      <td>20.074074</td>\n",
       "      <td>14170.500000</td>\n",
       "      <td>67.592593</td>\n",
       "      <td>0.314815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1813.234679</td>\n",
       "      <td>1287.165919</td>\n",
       "      <td>491.044843</td>\n",
       "      <td>25.309417</td>\n",
       "      <td>53.470852</td>\n",
       "      <td>2188.548580</td>\n",
       "      <td>595.458894</td>\n",
       "      <td>10395.708520</td>\n",
       "      <td>4311.364723</td>\n",
       "      <td>541.982063</td>\n",
       "      <td>1280.336323</td>\n",
       "      <td>70.442451</td>\n",
       "      <td>77.825112</td>\n",
       "      <td>14.099701</td>\n",
       "      <td>23.174888</td>\n",
       "      <td>8932.046338</td>\n",
       "      <td>65.092676</td>\n",
       "      <td>0.793722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Apps       Accept       Enroll  Top10perc  Top25perc   F.Undergrad  \\\n",
       "0  10363.138889  6550.898148  2569.722222  41.490741  70.203704  13061.935185   \n",
       "1   1813.234679  1287.165919   491.044843  25.309417  53.470852   2188.548580   \n",
       "\n",
       "   P.Undergrad      Outstate   Room.Board       Books     Personal        PhD  \\\n",
       "0  2464.861111  10719.175926  4643.472222  595.212963  1714.203704  86.398148   \n",
       "1   595.458894  10395.708520  4311.364723  541.982063  1280.336323  70.442451   \n",
       "\n",
       "    Terminal  S.F.Ratio  perc.alumni        Expend  Grad.Rate   Cluster  \n",
       "0  91.333333  14.027778    20.074074  14170.500000  67.592593  0.314815  \n",
       "1  77.825112  14.099701    23.174888   8932.046338  65.092676  0.793722  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_desc=pd.DataFrame(df.describe())\n",
    "feat = list(df_desc.columns)\n",
    "kmclus = pd.DataFrame(clus_cent,columns=feat)\n",
    "kmclus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What are the cluster labels?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
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       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
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      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation\n",
    "\n",
    "There is no perfect way to evaluate clustering if you don't have the labels, however since this is just an exercise, we do have the labels, so we take advantage of this to evaluate our clusters, keep in mind, you usually won't have this luxury in the real world.\n",
    "\n",
    "** Create a new column for df called 'Cluster', which is a 1 for a Private school, and a 0 for a public school.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def converter(cluster):\n",
    "    if cluster=='Yes':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1=df # Create a copy of data frame so that original data frame does not get 'corrupted' with the cluster index\n",
    "df1['Cluster'] = df['Private'].apply(converter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
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       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abilene Christian University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1660</td>\n",
       "      <td>1232</td>\n",
       "      <td>721</td>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>2885</td>\n",
       "      <td>537</td>\n",
       "      <td>7440</td>\n",
       "      <td>3300</td>\n",
       "      <td>450</td>\n",
       "      <td>2200</td>\n",
       "      <td>70</td>\n",
       "      <td>78</td>\n",
       "      <td>18.1</td>\n",
       "      <td>12</td>\n",
       "      <td>7041</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adelphi University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>2186</td>\n",
       "      <td>1924</td>\n",
       "      <td>512</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>2683</td>\n",
       "      <td>1227</td>\n",
       "      <td>12280</td>\n",
       "      <td>6450</td>\n",
       "      <td>750</td>\n",
       "      <td>1500</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>12.2</td>\n",
       "      <td>16</td>\n",
       "      <td>10527</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adrian College</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1428</td>\n",
       "      <td>1097</td>\n",
       "      <td>336</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>1036</td>\n",
       "      <td>99</td>\n",
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       "      <td>3750</td>\n",
       "      <td>400</td>\n",
       "      <td>1165</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>12.9</td>\n",
       "      <td>30</td>\n",
       "      <td>8735</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agnes Scott College</th>\n",
       "      <td>Yes</td>\n",
       "      <td>417</td>\n",
       "      <td>349</td>\n",
       "      <td>137</td>\n",
       "      <td>60</td>\n",
       "      <td>89</td>\n",
       "      <td>510</td>\n",
       "      <td>63</td>\n",
       "      <td>12960</td>\n",
       "      <td>5450</td>\n",
       "      <td>450</td>\n",
       "      <td>875</td>\n",
       "      <td>92</td>\n",
       "      <td>97</td>\n",
       "      <td>7.7</td>\n",
       "      <td>37</td>\n",
       "      <td>19016</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska Pacific University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>249</td>\n",
       "      <td>869</td>\n",
       "      <td>7560</td>\n",
       "      <td>4120</td>\n",
       "      <td>800</td>\n",
       "      <td>1500</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2</td>\n",
       "      <td>10922</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                             Private  Apps  Accept  Enroll  Top10perc  \\\n",
       "Abilene Christian University     Yes  1660    1232     721         23   \n",
       "Adelphi University               Yes  2186    1924     512         16   \n",
       "Adrian College                   Yes  1428    1097     336         22   \n",
       "Agnes Scott College              Yes   417     349     137         60   \n",
       "Alaska Pacific University        Yes   193     146      55         16   \n",
       "\n",
       "                              Top25perc  F.Undergrad  P.Undergrad  Outstate  \\\n",
       "Abilene Christian University         52         2885          537      7440   \n",
       "Adelphi University                   29         2683         1227     12280   \n",
       "Adrian College                       50         1036           99     11250   \n",
       "Agnes Scott College                  89          510           63     12960   \n",
       "Alaska Pacific University            44          249          869      7560   \n",
       "\n",
       "                              Room.Board  Books  Personal  PhD  Terminal  \\\n",
       "Abilene Christian University        3300    450      2200   70        78   \n",
       "Adelphi University                  6450    750      1500   29        30   \n",
       "Adrian College                      3750    400      1165   53        66   \n",
       "Agnes Scott College                 5450    450       875   92        97   \n",
       "Alaska Pacific University           4120    800      1500   76        72   \n",
       "\n",
       "                              S.F.Ratio  perc.alumni  Expend  Grad.Rate  \\\n",
       "Abilene Christian University       18.1           12    7041         60   \n",
       "Adelphi University                 12.2           16   10527         56   \n",
       "Adrian College                     12.9           30    8735         54   \n",
       "Agnes Scott College                 7.7           37   19016         59   \n",
       "Alaska Pacific University          11.9            2   10922         15   \n",
       "\n",
       "                              Cluster  \n",
       "Abilene Christian University        1  \n",
       "Adelphi University                  1  \n",
       "Adrian College                      1  \n",
       "Agnes Scott College                 1  \n",
       "Alaska Pacific University           1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create a confusion matrix and classification report to see how well the Kmeans clustering worked without being given any labels.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 74 138]\n",
      " [ 34 531]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.69      0.35      0.46       212\n",
      "          1       0.79      0.94      0.86       565\n",
      "\n",
      "avg / total       0.76      0.78      0.75       777\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "print(confusion_matrix(df1['Cluster'],kmeans.labels_))\n",
    "print(classification_report(df1['Cluster'],kmeans.labels_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering performance (e.g. distance between centroids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create two data frames consisting of only private or public university data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pvt=df[df['Private']=='Yes']\n",
    "df_pub=df[df['Private']=='No']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Play with parameters such as max_iter and n_init and calculate cluster centroid distances**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>K-means cluster centroid-distance</th>\n",
       "      <th>Mean of corresponding entity (private)</th>\n",
       "      <th>Mean of corresponding entity (public)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apps</th>\n",
       "      <td>8549.904210</td>\n",
       "      <td>1977.929204</td>\n",
       "      <td>5729.919811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accept</th>\n",
       "      <td>5263.732229</td>\n",
       "      <td>1305.702655</td>\n",
       "      <td>3919.287736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enroll</th>\n",
       "      <td>2078.677379</td>\n",
       "      <td>456.945133</td>\n",
       "      <td>1640.872642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Top10perc</th>\n",
       "      <td>16.181324</td>\n",
       "      <td>29.330973</td>\n",
       "      <td>22.834906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Top25perc</th>\n",
       "      <td>16.732852</td>\n",
       "      <td>56.957522</td>\n",
       "      <td>52.702830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F.Undergrad</th>\n",
       "      <td>10873.386605</td>\n",
       "      <td>1872.168142</td>\n",
       "      <td>8571.004717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P.Undergrad</th>\n",
       "      <td>1869.402217</td>\n",
       "      <td>433.966372</td>\n",
       "      <td>1978.188679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outstate</th>\n",
       "      <td>323.467406</td>\n",
       "      <td>11801.693805</td>\n",
       "      <td>6813.410377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Room.Board</th>\n",
       "      <td>332.107499</td>\n",
       "      <td>4586.143363</td>\n",
       "      <td>3748.240566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Books</th>\n",
       "      <td>53.230900</td>\n",
       "      <td>547.506195</td>\n",
       "      <td>554.377358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personal</th>\n",
       "      <td>433.867381</td>\n",
       "      <td>1214.440708</td>\n",
       "      <td>1676.981132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhD</th>\n",
       "      <td>15.955697</td>\n",
       "      <td>71.093805</td>\n",
       "      <td>76.834906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Terminal</th>\n",
       "      <td>13.508221</td>\n",
       "      <td>78.534513</td>\n",
       "      <td>82.816038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <td>-0.071923</td>\n",
       "      <td>12.945487</td>\n",
       "      <td>17.139151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perc.alumni</th>\n",
       "      <td>-3.100814</td>\n",
       "      <td>25.890265</td>\n",
       "      <td>14.358491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expend</th>\n",
       "      <td>5238.453662</td>\n",
       "      <td>10486.353982</td>\n",
       "      <td>7458.316038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grad.Rate</th>\n",
       "      <td>2.499917</td>\n",
       "      <td>68.966372</td>\n",
       "      <td>56.042453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             K-means cluster centroid-distance  \\\n",
       "Apps                               8549.904210   \n",
       "Accept                             5263.732229   \n",
       "Enroll                             2078.677379   \n",
       "Top10perc                            16.181324   \n",
       "Top25perc                            16.732852   \n",
       "F.Undergrad                       10873.386605   \n",
       "P.Undergrad                        1869.402217   \n",
       "Outstate                            323.467406   \n",
       "Room.Board                          332.107499   \n",
       "Books                                53.230900   \n",
       "Personal                            433.867381   \n",
       "PhD                                  15.955697   \n",
       "Terminal                             13.508221   \n",
       "S.F.Ratio                            -0.071923   \n",
       "perc.alumni                          -3.100814   \n",
       "Expend                             5238.453662   \n",
       "Grad.Rate                             2.499917   \n",
       "\n",
       "             Mean of corresponding entity (private)  \\\n",
       "Apps                                    1977.929204   \n",
       "Accept                                  1305.702655   \n",
       "Enroll                                   456.945133   \n",
       "Top10perc                                 29.330973   \n",
       "Top25perc                                 56.957522   \n",
       "F.Undergrad                             1872.168142   \n",
       "P.Undergrad                              433.966372   \n",
       "Outstate                               11801.693805   \n",
       "Room.Board                              4586.143363   \n",
       "Books                                    547.506195   \n",
       "Personal                                1214.440708   \n",
       "PhD                                       71.093805   \n",
       "Terminal                                  78.534513   \n",
       "S.F.Ratio                                 12.945487   \n",
       "perc.alumni                               25.890265   \n",
       "Expend                                 10486.353982   \n",
       "Grad.Rate                                 68.966372   \n",
       "\n",
       "             Mean of corresponding entity (public)  \n",
       "Apps                                   5729.919811  \n",
       "Accept                                 3919.287736  \n",
       "Enroll                                 1640.872642  \n",
       "Top10perc                                22.834906  \n",
       "Top25perc                                52.702830  \n",
       "F.Undergrad                            8571.004717  \n",
       "P.Undergrad                            1978.188679  \n",
       "Outstate                               6813.410377  \n",
       "Room.Board                             3748.240566  \n",
       "Books                                   554.377358  \n",
       "Personal                               1676.981132  \n",
       "PhD                                      76.834906  \n",
       "Terminal                                 82.816038  \n",
       "S.F.Ratio                                17.139151  \n",
       "perc.alumni                              14.358491  \n",
       "Expend                                 7458.316038  \n",
       "Grad.Rate                                56.042453  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=2,verbose=0,tol=1e-3,max_iter=50,n_init=10)\n",
    "kmeans.fit(df.drop('Private',axis=1))\n",
    "clus_cent=kmeans.cluster_centers_\n",
    "df_desc=pd.DataFrame(df.describe())\n",
    "feat = list(df_desc.columns)\n",
    "kmclus = pd.DataFrame(clus_cent,columns=feat)\n",
    "a=np.array(kmclus.diff().iloc[1])\n",
    "centroid_diff = pd.DataFrame(a,columns=['K-means cluster centroid-distance'],index=df_desc.columns)\n",
    "centroid_diff['Mean of corresponding entity (private)']=np.array(df_pvt.mean())\n",
    "centroid_diff['Mean of corresponding entity (public)']=np.array(df_pub.mean())\n",
    "centroid_diff"
   ]
  }
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